Wear measurement system using a computer model

ABSTRACT

A wear measurement system for a component is disclosed. The wear measurement system may include an imaging device configured to obtain a plurality of two-dimensional images of the component. The wear measurement system may also include a controller. The controller may be configured to generate a three-dimensional point cloud representing the component based on the two-dimensional images. The controller may also be configured to overlay a computer model of the component on the three-dimensional point cloud. Further, the controller may be configured to project the computer model on the two-dimensional images. The controller may also be configured to select at least two reference points appearing in each of a subset of images selected from the two-dimensional images. The controller may be configured to determine locations of the two reference points in the three-dimensional point cloud, determine an image distance between the locations, and determine an amount of wear based on the image distance.

TECHNICAL FIELD

The present disclosure relates generally to a wear measurement system,and, more particularly, to a wear measurement system using a computermodel.

BACKGROUND

Earth-working machines, for example, excavators, continuous miners, andloaders, often include ground engaging work tools that engage withand/or move a variety of earthen materials. Furthermore, trackedundercarriages that facilitate movement of the machines over groundsurfaces and other moving parts of these machines may engage with theground surface or earthen materials. Repeated exposure to hard workmaterials or the ground surface may cause one or more components ofthese machines to wear.

Conventional techniques for detecting wear on the machine componentsinclude manual measurements of component dimensions, which may becompared against specified dimensions of the components. Such manualmeasurements are not only time consuming but also can be inaccurate.Inaccurate measurements of the component dimensions in turn may resultin incorrect predictions regarding the remaining life of the component.As a result, the component may either fail too early or may not be wornenough to require replacement or repair when the machine is removed fromservice for maintenance. Thus, there is a need for accurate measurementof component dimensions on a machine in its work environment to allowfor improved component life predictions, which may help reduce the downtime associated with repair or replacement of worn out components.

U.S. Pat. No. 7,327,857 B2 to Lloyd Jr. et al. (“the '857 patent”) thatissued on Feb. 5, 2008 discloses a non-contact measurement method andapparatus for examining an object having complex surfaces or shapedeformations. The '857 patent discloses an imaging device, such as, afull-field, non-contact, laser range sensor mounted on a translationstage that provides data in three spatial dimensions (X, Y, Z) of anobject. The '857 patent refers to this three-dimensional data as ascanned image. The '857 patent further discloses that athree-dimensional reference model or reference image of the object isstored in a memory. The '857 patent discloses that the reference modelis registered with the three-dimensional scanned image of the object. Inaddition, the '857 patent discloses a gauging module that determines oneor more gauging measurements as the spacing between contours of thescanned image and the reference model.

Although the '857 patent discloses the use of imaging for measurement ofdeformations on a component, the disclosed device and methods may stillnot be optimal. In particular, the disclosed device requires the use ofa specialized device mounted on a translation stage and configured togenerate three-dimensional spatial measurements of the component. Theuse of a specialized imaging device and stage may make the disclosedapparatus cumbersome to use and expensive to maintain. Moreover, thedisclosed apparatus requires the component to be taken out of servicefor performing the measurements. Such down time may not be practical ordesirable to ensure maximum utilization of the component.

The wear measurement system of the present disclosure solves one or moreof the problems set forth above and/or other problems in the art.

SUMMARY

In one aspect, the present disclosure is directed to a wear measurementsystem for a component. The wear measurement system may include animaging device configured to obtain a plurality of two-dimensionalimages of the component. The wear measurement system may also include acontroller. The controller may be configured to generate athree-dimensional point cloud representing the component based on thetwo-dimensional images. The controller may also be configured to overlaya computer model of the component on the three-dimensional point cloud.Further, the controller may be configured to project the computer modelon the two-dimensional images. The controller may also be configured toselect at least two reference points appearing in each of a subset ofimages selected from the two-dimensional images. The controller may beconfigured to determine locations of the two reference points in thethree-dimensional point cloud. The controller may also be configured todetermine an image distance between the locations. In addition, thecontroller may be configured to determine an amount of wear based on theimage distance.

In another aspect, the present disclosure is directed to a method ofmeasuring wear on a component. The method may include obtaining, usingan imaging device, a plurality of two-dimensional images of thecomponent. The method may also include generating, using a controller, athree-dimensional point cloud representing the component based on thetwo-dimensional images. Further, the method may include overlaying acomputer model of the component on the three-dimensional point cloud.The method may also include projecting the computer model on thetwo-dimensional images. The method may include selecting at least tworeference points appearing in each of a subset of images selected fromthe two-dimensional images. The method may also include determininglocations of the at least two reference points in the three-dimensionalpoint cloud. The method may include determining an image distancebetween the locations. In addition, the method may include determiningan amount of wear based on the image distance.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a pictorial illustration of an exemplary disclosed machine andan arrangement of imaging devices for obtaining images of the machine;

FIG. 2 is a schematic illustration of an exemplary disclosed wearmeasurement system that may be used with the machine of FIG. 1;

FIG. 3 is a flow chart illustrating an exemplary disclosed wearmeasurement method performed by the wear measurement system of FIG. 2;

FIG. 4 is a pictorial illustration of an exemplary arrangement forgenerating a three-dimensional image using two-dimensional imagesobtained using the wear measurement system of FIG. 2;

FIG. 5 is a pictorial illustration of an exemplary two-dimensional imageobtained using the wear measurement system of FIG. 2;

FIG. 6 is a pictorial illustration of the exemplary locations of thereference points of FIG. 5 projected on the three-dimensional image ofFIG. 4;

FIG. 7 is a pictorial illustration of an exemplary disclosedundercarriage overlaid with locations of reference points used formaking exemplary dimensional measurements;

FIG. 8 is flow chart illustrating another exemplary disclosed wearmeasurement method performed by the wear measurement system of FIG. 2;and

FIG. 9 is a pictorial illustration of exemplary reference points formaking dimensional measurement using a computer model.

DETAILED DESCRIPTION

FIG. 1 illustrates an exemplary embodiment of a machine 10 having anundercarriage 12, which may be used to propel machine 10 in a forward orrearward direction. Machine 10 may perform some type of operationassociated with an industry such as construction, mining, or anotherindustry known in the art. For example, as illustrated in FIG. 1,machine 10 may be a loader. It is contemplated however that machine 10may be an excavator, a tractor, a continuous mining machine, a tank, oranother machine having track-type traction devices.

Undercarriage 12 may be configured to support machine 10 and may engagethe ground, roads, and/or other types of terrain. Undercarriage 12 mayinclude, among other things, frame 14, track 16, sprocket wheel 18,idler wheel 20, one or more upper rollers 22, and one or more lowerrollers 24. In some exemplary embodiments, undercarriage 12 may alsoinclude one or more sliders in place of or in addition to upper rollers22. Sprocket wheel 18, idler wheel 20, upper rollers 22, and lowerrollers 24 may be attached to frame 14 of undercarriage 12. Track 16 maywrap around sprocket wheel 18, idler wheel 20, upper rollers 22, andlower rollers 24 to form an endless chain. Track 16 may include aplurality of individual links 26 connected end-to-end via pins 28

Sprocket wheel 18 and idler wheel 20 may be located on opposite ends ofundercarriage 12. For example, as illustrated in FIG. 1, idler wheel 20may be located adjacent front end 30 of frame 14 and sprocket wheel 18may be located adjacent rear end 32 of frame 14. Sprocket wheel 18 mayinclude one or more projections (or teeth) 34 that engage with track 16and transfer tractive forces from sprocket wheel 18 to track 16.Sprocket wheel 18 may be rotated by power source 36 of machine 10.

Power source 36 may be an engine, which may generate a power output thatcan be directed through sprocket wheel 18 and track 16 to propel machine10 in a forward or rearward direction. For example, power source 36 maybe any suitable type of internal combustion engine, such as a gasoline,diesel, natural gas, or hybrid-powered engine. It is also contemplatedhowever that power source 36 may be driven by electrical power. Powersource 36 may be configured to deliver power output directly to sprocketwheel 18. Additionally or alternatively, power source 36 may beconfigured to deliver power output to a generator (not shown), which mayin turn drive one or more electric motors (not shown) coupled tosprocket wheel 18. According to yet another embodiment, power source 36may deliver power output to a hydraulic motor (not shown) fluidlycoupled to a hydraulic pump (not shown) and configured to convert afluid pressurized by the pump into a torque output, which may bedirected to sprocket wheel 18. Power source 36 may also provide power tomove and/or manipulate work tool 38 associated with machine 10.

Upper and lower rollers 22, 24 may guide track 16 between sprocket wheel18 and idler wheel 20. For example, upper roller 22 may guide track 16at an upper track side 40 of frame 14. To do so, upper roller 22 mayextend upward from frame 14 and engage an inner portion of links 26.Lower rollers 24 may guide track 16 at a lower track side 42 of frame14. Lower rollers 24 may each be suspended from frame 14. Lower rollers24 may ride on and guide links 26 as track 16 travels around sprocketwheel 18 and idler wheel 20.

FIG. 1 also illustrates imaging devices 44, which may be configured totake two-dimensional (2D) images of machine 10 or of undercarriage 12.As illustrated in FIG. 1, imaging device 44 may be located at differentpositions 46, 48, 50, 52 to allow imaging device 44 to take a pluralityof two-dimensional images of machine 10 or of undercarriage 12 fromdifferent positions and orientations. Although only four positions 46,48, 50, 52 are illustrated in FIG. 1, it is contemplated that 2D imagesof machine 10 or of undercarriage 12 may be obtained from fewer than ormore than four positions. Further, although the same imaging device 44has been shown at each of the positions 46, 48, 50, 52, it iscontemplated that different imaging devices 44 could be located atdifferent positions 46, 48, 50, 52 to obtain the plurality of 2D images.

In one exemplary embodiment, imaging device 44 may be a digital cameraattached to a movable frame (not shown), which may allow a same imagingdevice 44 to be moved horizontally and/or vertically around machine 10to positions 46, 48, 50, 52. Movable frame may also allow imaging device44 to be rotated, allowing imaging device 44 to be located at differentdistances and orientations relative to machine 10. In another exemplaryembodiment, imaging device 44 may be a digital camera operated by anoperator capable of moving to different positions relative to machine10. The operator may be able to acquire a plurality of 2D images ofmachine 10 and/or undercarriage 12 from different distances and fromdifferent orientations. In yet another exemplary embodiment, imagingdevice 44 may be a smartphone equipped with a camera, a tablet deviceequipped with a camera, a computer equipped with a camera, or any othertype of electronic equipment known in the art equipped with a camera.

FIG. 2 illustrates a schematic view of an exemplary wear measurementsystem 60 that may be used with machine 10 or with a machine component,for example, undercarriage 12. Wear measurement system 60 may includeimaging device 44, controller 62, display device 64, input device 66,alert device 68, and database 70. Imaging device 44 may include imageprocessor 72, memory 74, and antenna 76. Image processor 72 may beconfigured to acquire one or more images 78 of a machine component, forexample, undercarriage 12, and store images 78 in the form of anelectronic data file in memory 74. Images 78 may be two-dimensionalstill images or may include two-dimensional video images. Imageprocessor 72 may be configured to control antenna 76 to wirelesslytransmit the one or more electronic data files representing images 78 tocontroller 62. Image processor 72 may transmit the one or more images 78through antenna 76 to controller 62 before or after storing images 78,or even without storing images 78 in memory 74. It is also contemplatedthat imaging device 44 may transmit or transfer the one or more images78 to controller 62 through a wired connection or using other ways oftransferring digital information known in the art. Although FIG. 2illustrates only one image processor 72, one memory 74, and one antenna76, it is contemplated that imaging device 44 may have any number ofimage processors 72, memories 74, and antennae 76.

Controller 62 may include processor 82, memory 84, storage device 86,and antenna 88. Processor 82 may be configured to control operations ofmemory 84, storage device 86, and antenna 88. Antenna 88 of controller62 may be configured to wirelessly receive the one or more images 78transmitted from imaging device 44 via antenna 76. Memory 84 or storagedevice 86 may store the images 78 received by antenna 88. Memory 84 orstorage device 86 may also store instructions that processor 82 may beconfigured to execute to perform a variety of operations on images 78received from imaging device 44. Although FIG. 2 illustrates only oneprocessor 82, one memory 84, one storage device 86, and one antenna 88,it is contemplated that controller 62 may include any number ofprocessors 82, memories 84, storage devices 86, and antennae 88.

Image processor 72 and processor 82 may each embody a single or multiplemicroprocessors, digital signal processors (DSPs), etc. Numerouscommercially available microprocessors can be configured to perform thefunctions of each of image processor 72 and processor 82. Various otherknown circuits may be associated with each of image processor 72 andprocessor 82, including power supply circuitry, signal-conditioningcircuitry, and communication circuitry. Memories 74, 84 may embodynon-transitory computer-readable media, for example, Random AccessMemory (RAM) devices, NOR or NAND flash memory devices, and Read OnlyMemory (ROM) devices. Storage device 86 may embody non-transitorycomputer-readable media, such as, RAM, NOR, NAND, or ROM devices,CD-ROMs, hard disks, floppy drives, optical media, solid state storagemedia, etc.

One or more display devices 64 may be associated with controller 62 andmay be configured to display data or information in cooperation withprocessor 82. For example, display device 64 may be configured todisplay the one or more 2D images 78 received by controller 62 fromimaging device 44. Display device 64 may also be configured to displayinformation generated by processor 82 as a result of operationsperformed on the one or more images 78. Display device 64 may be acathode ray tube (CRT) monitor, a liquid crystal display (LCD), a lightemitting diode (LED) display, a projector, a projection television set,a touchscreen display, or any other kind of display device known in theart.

One or more input devices 66 may also be associated with controller 62and may be configured to receive inputs from an operator 90 of wearmeasurement system 60. Operator 90 may also be capable of operatingimaging device 44. It is also contemplated that different operators 90may operate imaging device 44 and controller 62. Processor 82 mayreceive inputs from the operator 90 via input device 66 and may performoperations on one or more images 78 based on the received inputs. In oneexemplary embodiment, input device 66 may enable an operator 90 of wearmeasurement system 60 to make selections of one or more portions of theone or more images 78. Input device 66 may also enable the operator toprovide numerical, textual, graphic, or audio-visual inputs to processor82. Input device 66 may include a physical keyboard, virtualtouch-screen keyboard, mouse, joystick, stylus, etc. In certainembodiments, input device 66 may also include one or more microphones(not shown) using, for example, speech-to-text and/or voice recognitionapplications. Although, controller 62, display device 64, input device66, and database 70 have been described separately, it is contemplatedthat controller 62, display device 64, input device 66, and database 70may form a desktop computer system, a server system, a laptop computersystem, or any other type of computing system known in the art. It isalso contemplated that controller 62, display device 64, input device66, and database 70 may be part of a server farm consisting of aplurality of servers.

Alert device 68 may be associated with controller 62 and may beconfigured to generate an audible, a visual, or an audio-visual alertbased on instructions received from processor 82. Alert device 68 may bea separate device or may be incorporated in display device 64 to provideaudio-visual alerts to operator 90. Database 70 may be associated withcontroller 62 and may be configured to store instructions for executionby processor 82. Database 70 may also be configured to store the one ormore images 78, inputs received from input device 66, and/or images,data, or other information generated as a result of operations performedby processor 82 on the one or more images 78.

INDUSTRIAL APPLICABILITY

The wear measurement system of the present disclosure may be used toperform measurement of components on a wide variety of machines. Inparticular, the wear measurement system of the present disclosure may beused to obtain two-dimensional images of an entire machine or of aparticular component of the machine, and to obtain dimensionalmeasurements using the two-dimensional images without removing themachine from service. The measured dimensions may be compared tospecified dimensions to determine amounts of wear on one or morecomponents included in the two-dimensional images. Exemplary methods ofoperation of wear measurement system 60 will be discussed below.

FIG. 3 illustrates an exemplary method 1000 of performing wearmeasurement on a component, for example, undercarriage 12 of machine 10.For ease of explanation, method 1000 is described with respect toundercarriage 12 of machine 10. One of ordinary skill in the art wouldrecognize however that method 1000 may be similarly applied to any othercomponent of machine 10 or to components of a different machine.

Method 1000 may include a step of acquiring 2D images 78 of a component,such as, undercarriage 12 (Step 1002). As discussed above, imagingdevice 44 may be used to acquire 2D images 78 of undercarriage 12 fromdifferent positions (e.g. positions 46, 48, 50, 52). Further, imagingdevice 44 may be disposed at different orientations (i.e. rotations withrespect to the coordinate axes associated with imaging device 44) at thedifferent positions 46, 48, 50, 52. Images 78 acquired by imaging device44 may be stored in memory 74 of imaging device 44. It is alsocontemplated that images 78 may be stored in memory 84 of controller 62or in database 70. In one exemplary embodiment, each of images 78 mayinclude a two-dimensional view of undercarriage 12. In another exemplaryembodiment, images 78 may constitute a two-dimensional video ofundercarriage 12, including views from different camera positions ororientations.

Method 1000 may include a step of generating a three-dimensional (3D)image of undercarriage 12 based on 2D images 78 (Step 1004). When images78 constitute a two-dimensional video, processor 82 of controller 62 maybe configured to extract 2D images of undercarriage 12 from thetwo-dimensional video. FIG. 4 illustrates an exemplary arrangement forgenerating 3D point cloud 92, using 2D images 78 obtained using imagingdevice 44. As illustrated in FIG. 4, exemplary 2D images 94, 96, 98 maycorrespond to positions 46, 48, 50, respectively, of imaging device 44.Processor 82 of controller 62 may be configured to determine positions46, 48, 50 and orientations of imaging device 44 at each of thepositions 46, 48, 50 in a coordinate system associated with wearmeasurement system 60. Positions 46, 48, 50 and orientations of imagingdevice 44 may constitute poses of imaging device 44. Processor 82 maydetermine the poses of imaging device 44 at each of the positions 46,48, 50 based on a comparison of matching features, such as, 100, 102that appear in each of 2D images 94, 96, 98. Processor 82 may determinethe poses by comparing the relative positions of features 100, 102 in 2Dimages 94, 96, 98. Processor 82 may determine the positions of features100, 102 in 2D images 94, 96, 98 using triangulation techniques. In oneexemplary embodiment, processor 82 may sequentially refine the positionsof features 100, 102 in 2D images 94, 96, 98, by updating the positionsas each 2D image is processed.

Matching features 100, 102 may be selected in many different ways. Inone exemplary embodiment, an operator 90 of wear measurement system 60may view images 94, 96, 98 on display device 64 and use one or moreinput devices 66 to identify and select matching features 100, 102 ineach of 2D images 94, 96, 98. For example, operator 90 may use a mouseto select matching features 100, 102 in each of 2D images 94, 96, 98.Matching features 100, 102 may include one or more distinguishablefeatures of the imaged component. For example, in 2D images 94, 96, 98of undercarriage 12, matching features 100, 102 may include pins 28connecting adjacent links 26 of track 16, sprocket wheel 18, idler wheel20, upper and/or lower rollers 22, 24, etc.

In another exemplary embodiment, processor 82 may use feature matchingalgorithms to automatically identify and select matching features 100,102 in each of 2D images 94, 96, 98. For example, processor 82 mayperform a comparison of a portion of or an entirety of images 94, 96, 98with images of features 100, 102 stored in memory 84, storage device 86,and/or database 70 to identify features 100, 102 in each of 2D images94, 96, 98.

In step 1004 of method 1000, processor 82 of controller 62 may generatepoint cloud image 92 using triangulation and optimization techniques.For example, processor 82 may generate point cloud image based on theposes (positions and orientations) of imaging devices 44 and therelative positions of matching features 100, 102 in 2D images 94, 96,98.

Although FIG. 4 illustrates only three images 94, 96, 98 at threepositions 46, 48, 50, and only two matching features 100, 102, it iscontemplated that any number of images 78 at any number of positions(e.g. 46, 48, 50), and any number of features (e.g. 100, 102) may beused to generate 3D point cloud 92 using 2D images 78. One of ordinaryskill in the art would recognize that locations 110, 118, etc. in 3Dpoint cloud 92 may appear as point representations of features 100, 102,respectively. One of ordinary skill in the art would also recognize that3D point cloud 92 may not include continuous edges, but may insteadinclude a cluster of locations representing discrete reference points in2D images 78. As a result 3D point cloud 92 may be an image thatincludes a cloud of points representing locations of discrete referencepoints in 2D images 78.

Returning to FIG. 3, method 1000 may include a step of selecting asubset of 2D images 78 from the plurality of images 78 of undercarriage12 obtained by imaging device 44 (Step 1006). In one exemplaryembodiment, operator 90 may view each of the plurality of images 78 ondisplay device 64 and use input device 66 to select the subset of images78. For example, the plurality of 2D images 78 may include more thanfifty images and operator 90 may select a subset of four 2D images 78from the more than fifty 2D images 78. In another exemplary embodiment,processor 82 may use, for example, a randomization algorithm to randomlyselect a subset of 2D images 78 from the plurality of images 78.

Method 1000 may include a step of selecting reference points in thesubset of 2D image 78 (Step 1008). In one exemplary embodiment, operator90 may view each of the subset of 2D images 78 on display device 64 anduse input device 66, to identify and select reference points on each ofthe subset of 2D images 78. FIG. 5 illustrates an exemplary 2D image 94from the subset of 2D images 78 of undercarriage 12 selected by operator90. As illustrated in FIG. 5, operator 90 may select, for example,center points 120-202 of pins 28 that connect adjacent links 26 ofundercarriage 12. Operator 90 may identify and select center points120-202 of pins 28 on each of the subset of 2D images 78 selected, forexample, in step 1006. It is contemplated that operator 90 may selectany number of reference points in each of the subset of 2D images 78.

Returning to FIG. 3, method 1000 may include a step of determiningreference point locations on 3D point cloud 92 (Step 1010) generated,for example, in step 1004. FIG. 6 illustrates a subset of four 2D images94, 96, 210, 212 and the associated positions 46, 48, 214, 216, ofimaging device 44 for each of the four images 94, 96, 210, 212,respectively. Processor 82 may project lines from each of positions 46,48, 214, 216 through reference points 120-202 in each of the associated2D images 94, 96, 210, 212, respectively. Thus, for example, processor82 may project ray 218 from position 46 through reference point 132 (seealso FIG. 5) in 2D image 94. Processor 82 may similarly project ray 220from position 48 through reference point 132 in 2D image 96. Processor82 may repeat this process for 2D images 210, 212. Processor 82 maydetermine, for example, location 222 of reference point 132 on 3D pointcloud 224 based on an intersection of rays 218, 220 and similar raysfrom positions 214, 216 corresponding to 2D images 210, 212,respectively. Because of potential differences in imaging conditions forimages 94, 96, 210, 212 and inherent errors associated with imagingobjects, rays 218, 220 and rays from positions 214, 216 passing throughreference point 132 may not converge at a single location 222 inthree-dimensional space. In such cases, processor 82 may determinelocation 222 of reference point 132 in 3D point cloud 224 as theposition where a measure of distance between rays 218, 220 and rays frompositions 214, 216 passing through reference point 132 is less than athreshold distance. Processor 82 may repeat these steps for each of thereference points 120-202 (see FIG. 5) to determine the locations of eachof the reference points 120-202 in 3D point cloud 224.

Returning to FIG. 3, method 1000 may include a step of determiningdistances between the reference points 120-202 (Step 1012). Processor 82may determine locations (e.g. 222, 226) in 3D point cloud 224 (see FIG.6) that are representative of reference points (e.g. 132, 134 in FIG.5). Processor 82 may further determine an image distance between thelocations (e.g. 222, 226). As used in this disclosure, image distancerepresents a distance between locations on a 3D point cloud (e.g. 224).One of ordinary skill in the art would recognize that image distancewould depend on the dimensions of the 3D point cloud. One of ordinaryskill in the art would also recognize that image distance would bedifferent from an actual distance between the reference points on anactual undercarriage 12 of machine 10. In one exemplary embodiment,processor 82 may determine the image distance as a number of pixelslocated between the locations (e.g. 222, 226 in FIG. 6) corresponding toreference points (e.g. 132, 134 in FIG. 5). In another exemplaryembodiment, processor 82 may determine the image distance in physicaldimensions (mm, cm, etc.) between the locations (e.g. 222, 226 in FIG.6) corresponding to the reference points (e.g. 132, 134 in FIG. 5) basedon, for example, dimensions of display device 64 or a size of pixels indisplay device 64. Processor 82 may also be configured to determine ascaling factor “S” to convert the image distance into an actual distancethat would be observed on undercarriage 12.

Processor 82 may determine the scaling factor S in a variety of ways. Inone exemplary embodiment, processor 82 may determine the scaling factorS based on the known dimensions of a feature 230 (see FIG. 5), which mayexist on undercarriage 12. For example, as illustrated in FIG. 5, frame14 of undercarriage 12 may include an embossed feature 230 of knownlength “L” extending between reference points 232, 234. Processor 82 mayidentify the locations 236, 238 (see FIG. 6) of reference points 232,234, on either end of the embossed feature 230, in 3D point cloud 224 ina manner similar to that described above with respect to referencepoints 120-202. Processor 82 may determine image distance “l” betweenlocations 236, 238 on 3D point cloud 224, corresponding to the referencepoints 232, 234. Processor 82 may use the known length L of the embossedstrip 230 and image distance l to determine the scaling factor S (e.g.S=L/l).

In another exemplary embodiment, a linear scale (e.g. tape measure) maybe placed on frame 14 of undercarriage 12 before acquiring the 2D images78. Operator 90 may identify two reference points on an image of thetape measure in step 1008 of method 1000. Processor 82 may determine theimage distance between the locations in 3D point cloud 224 correspondingto the two reference points on the tape measure. Processor 82 maydetermine the scaling factor S based on the determined image distanceand a known length of the tape measure between the two reference pointsselected by operator 90. Processor 82 may use the scaling factor S todetermine an actual distance that would be observed on an actualundercarriage 12.

FIG. 7 illustrates an exemplary 3D point cloud 224, showing locationsthat may be used to determine physical dimensions of an undercarriage12. For example, six sets 236-246 of locations are illustrated in FIG.7. Set 236 may include locations 252-268, corresponding to center pointsof pins 28 on upper track side 40 of undercarriage 12. Set 238 mayinclude locations 272, 274, corresponding to center points of two upperrollers 22. Set 240 may include locations 282-302, corresponding tocenter points of pins 28 on lower track side 42 of undercarriage 12. Set242 may include locations 312-324, corresponding to center points oflower rollers 24. Set 244 may include locations 332, 334, 336,corresponding to three reference points on idler wheel 20 ofundercarriage 12. Set 246 may include locations 342-348, correspondingto reference points on tooth 34 of sprocket wheel 18.

One of ordinary skill in the art would recognize that locations 252-268,272, 274, 282-302, 312-324, 332-336, and 343-348 may correspond toreference points selected in step 1008 as discussed above with respectto FIG. 5. For example, locations 252-268 of set 236 in 3D point cloud224 may correspond with reference points 142-160 in 2D image 94 shown inFIG. 5. Similarly, for example, locations 282-302 of set 240 in 3D pointcloud 224 may correspond to reference points 182-202 in 2D image 94.Processor 82 may determine actual distances, for example, pincenter-to-center distance “D_(pin),” slider distance “D_(slider),”roller distance “D_(roller),” idler radius “R_(idler),” idler location“D_(idler),” and drive tooth thicknesses “T_(base),” and “T_(tip)” (seeFIG. 5) based on the six sets 236-246 of locations 252-268, 272, 274,282-302, 312-324, 332-336, and 343-348 in 3D point cloud 224.

As illustrated in FIG. 5, D_(pin) may represent a distance between thecenter points of adjacent pins (e.g. 142, 144). Processor 82 maydetermine dimension D_(pin) by measuring an image distance d_(pin)between locations (e.g. 254, 256 selected from set 236 in FIG. 7)representing adjacent pairs of center points (e.g. 142, 144 in FIG. 5).Processor 82 may use scaling factor S to convert image distance d_(pin)to actual distance D_(pin) (e.g. D_(pin)=S×d_(pin)).

To determine dimension D_(slider) (see FIG. 5), processor 82 may fit astraight line 350 through locations 252-268 in set 230 and a straightline 352 through locations 312-324 in set 236 of 3D point cloud 224 (seeFIG. 7). Processor 82 may determine an image distance d_(slider), forexample, as a perpendicular distance between straight lines 350 and 352.Processor 82 may convert image distance d_(slider) into an actualdistance D_(slider), using the scaling factor S (e.g.D_(slider)=S×d_(slider)).

Similarly, to determine D_(roller), processor 82 may fit a straight line352 through locations 312-324 in set 236 and straight line 354 throughlocations 282-302 in set 234 of 3D point cloud 224. Processor 82 maydetermine an image distance d_(roller), for example, as a perpendiculardistance between straight lines 352 and 354. Processor 82 may convertimage distance d_(roller) into an actual distance D_(roller), using thescaling factor S (e.g. D_(roller)=S×d_(roller)).

As further illustrated in FIG. 7, to determine radius of idler wheel 20,R_(idler), processor 82 may fit a circle 356 that passes throughlocations 332-336 in set 238 of 3D point cloud 224 (see FIG. 7).Processor 82 may determine image distance r_(idler) as a radius of thefitted circle 356 and use the scaling factor S to convert image distancer_(idler) to actual distance R_(idler) (e.g. R_(idler)=S×r_(idler)).Processor 82 may also determine a distance, D_(idler), of idler wheel 20from a lower roller 24 by determining an image distance d_(idler)between location 312 and a center 358 of the fitted circle 356 in 3Dpoint cloud 224 (see FIG. 7). Processor 82 may convert image distanced_(idler) into actual distance using the scaling factor S (e.g.D_(idler)=S×d_(idler)).

Similarly, processor 82 may determine image distances t_(base) andt_(tip) as the distances between locations 342, 344 and 346, 348,respectively in 3D point cloud 224. Processor 82 may convert imagedistances t_(base) and t_(tip) into actual thickness T_(base) at a baseof tooth 34 and actual thickness T_(tip) at a tip of tooth 34, usingscaling factor S (e.g. T_(base)=S×t_(base) and T_(tip)=S×t_(tip)).

Returning to FIG. 3, method 1000 may include a step of determining anamount of wear (Step 1014). Processor 82 may determine amounts of wearby comparing the actual distances determined in, for example, step 1012with specified values of the corresponding dimensions. For example, thedimensions D_(pin), D_(slider), D_(roller), R_(idler), D_(idler),T_(base), and T_(tip) may have specified values D_(pin-S), D_(slider-S),D_(roller-S), R_(idler-S), D_(idler-S), T_(base-S), and T_(tip-S).Processor 82 may determine an amount of wear as a difference between theactual distances determined, for example, in step 1012 and the specifiedvalues. For example, an amount of pin wear Δ_(pin) may be determined byprocessor 82 as a difference between D_(pin-S) and D_(pin) (i.e.D_(pin-S)−D_(pin)). Processor 82 may similarly determine an amount ofslider wear Δ_(slider) (i.e. D_(slider-S)−D_(slider)), an amount ofroller wear Δ_(roller) (i.e. D_(roller-S)−D_(roller)), an amount ofidler wear Δ_(idler) (i.e. R_(idler-S)−R_(idler)), an amount of wear inidler location Δ_(idler-loc) (i.e. D_(idler-S)−D_(idler)), and amountsof drive tooth wear Δ_(base) (i.e. T_(base-S)−T_(base)) and Δ_(tip)(i.e. T_(tip-S)−T_(tip)). Although a simple difference between twovalues to determine an amount of wear is discussed above, it iscontemplated that processor 82 may use ratios, absolute values, or anyother mathematical functions or operations on the actual distances andthe corresponding specified values to determine the amounts of wear. Itis contemplated that operator 90 may identify and select fewer than ormore than six sets 236-246 of locations 252-268, 272, 274, 282-302,312-324, 332-336, and 343-348 in, for example, step 1008 of method 1000.It is further contemplated that processor 82 may determine fewer than ormore than the actual distances D_(pin), D_(slider), D_(roller),R_(idler), D_(idler), T_(base), and T_(tip), discussed above.

Method 1000 may also include a step of generating an alert when anamount of wear exceeds a threshold amount (Step 1016). Processor 82 maycompare one or more amounts of wear Δ_(pin), Δ_(slider), Δ_(roller),Δ_(idler), Δ_(idler-loc), Δ_(base), and Δ_(tip) with their respectivethreshold amount of wear Δ_(pin-limit), Δ_(slider-loc)Δ_(roller-limit),Δ_(idler-limit), Δ_(idler-loc-limit), Δ_(base-limit), and Δ_(tip-limit),respectively. When a determined amount of wear (Δ_(pin), Δ_(slider),Δ_(roller), Δ_(idler), Δ_(idler-loc), Δ_(base), or Δ_(tip)) exceeds acorresponding threshold amount (Δ_(pin-limit), Δ_(slider-limit),Δ_(roller-limit), Δ_(idler-limit), Δ_(idler-loc-limit), Δ_(base-limit),and Δ_(tip-limit)), processor 82 may signal alert device 68 to generatean alert, which may be audible, visual, or both. It is also contemplatedthat operator 90 may use the one or more amounts of wear Δ_(pin),Δ_(slider), Δ_(roller), Δ_(idler), Δ_(idler-loc), Δ_(base), and Δ_(tip)to determine when to order replacement parts or when to schedulemaintenance activities for machine 10. Thus, by determining amounts ofwear without taking machine 10 out of service, method 1000 may allowoperator 90 to schedule and perform maintenance on machine 10 in a moretimely and cost effective manner.

FIG. 8 illustrates another exemplary method 2000 of performing wearmeasurement on a component, for example, undercarriage 12 of machine 10.For ease of explanation, in the following, method 2000 is described withrespect to wear measurement on undercarriage 12 of machine 10. One ofordinary skill in the art would recognize however that method 2000 maybe similarly applied to any other component of machine 10 or tocomponents of a different machine.

Method 2000 may include a step of acquiring one or more 2D images 78 ofa component, such as, undercarriage 12 (Step 2002). Imaging device 44and processor 82 may perform operations similar to those discussed abovewith respect to, for example, step 1002 of method 1000 to acquire 2Dimages 78 of undercarriage 12. Method 2000 may also include a step ofgenerating a 3D point cloud 224 of undercarriage 12 based on the 2Dimages 78 (Step 2004). Processor 82 may perform operations similar tothose discussed above with respect to, for example, step 1002 of method1000 to generate 3D point cloud 224.

Method 2000 may include a step of registering a three-dimensionalcomputer model with 3D point cloud 224 (Step 2006). In one exemplaryembodiment, the computer model may include a computer-aided-design (CAD)representation of undercarriage 12. Processor 82 may overlay the 3Dcomputer model of undercarriage 12 on 3D point cloud 224 generated in,for example, step 2004. Processor 82 may select a first set of registerpoints in the 3D computer model. For example, the first set of registerpoints may include the center points of sprocket wheel 18, idler wheel20, pins 28, upper rollers 22, lower rollers 24, etc. Processor 82 mayselect a second set of register points corresponding to the first set ofregister points in 2D images 78. For example, processor 82 may selectcenter points of sprocket wheel 18, idler wheel 20, pins 28, upperrollers 22, lower rollers 24, etc., in 2D images 78 as the second set ofregister points. Processor 82 may project the second set of registerpoints on the 3D point cloud using operations similar to those discussedabove with respect to step 2004. Processor 82 may orient (i.e. scaleand/or rotate) 3D point cloud 224 such that the first set of registerpoints in the computer model overlap the locations corresponding to thesecond set of register points in 3D point cloud 224. In anotherexemplary embodiment, processor 82 may orient (i.e. scale and/or rotate)the 3D computer model such that the first set of register points in thecomputer model overlap the locations corresponding to the second set ofregister points in 3D point cloud 224. In one exemplary embodiment, thefirst set of register points and the second set of register points mayeach include at least four locations.

Method 2000 may include a step of projecting the 3D computer model onto2D images 78 (Step 2008) obtained, for example, in step 2002. In oneexemplary embodiment, processor 82 may project the 3D computer modelimage on a subset of 2D images 78 based on the position and orientation(pose) associated with each 2D image relative to the 3D point cloud 224of undercarriage 12. For example, referring to FIG. 6, processor 82 mayproject the computer model onto each of 2D images 94, 96, 210, 212 basedon their positions 46, 48, 214, 216, respectively, and the orientationsof imaging device 44 at positions 46, 48, 214, 216. FIG. 9 illustrates amagnified view of a portion of an exemplary 2D image 94 that includes aprojection of the 3D computer model. As illustrated in FIG. 9, edge 360of the computer model illustrates the as-designed shape of tooth 34 onsprocket wheel 18 of undercarriage 12. Edges 362 in FIG. 9 represent theactual shape of tooth 34 as observed in 2D image 94.

Returning to FIG. 8, method 2000 may include a step of selecting asubset of 2D images 78 with the projected computer model (Step 2010). Instep 2010, processor 82 may perform operations similar to thosediscussed above with respect to, for example, step 1006 of method 1000.Method 2000 may also include a step of selecting reference points in thesubset of 2D images 78 (Step 2012). In one exemplary embodiment,operator 90 may view each of the subset of 2D images 78 including aprojection of the computer model on display device 64 and use inputdevice 66, to identify and select reference points for making wearmeasurements on each of the subset of 2D images 78. As illustrated inFIG. 9, operator 90 may select, for example, two sets 364 and 366 ofreference points in 2D image 94. For example, operator 90 may select set364 of reference points 372-384 located on an outline of tooth 34 ofsprocket wheel 18 as visible in 2D image 94 obtained using imagingdevice 44. Operator 90 may also select set 366 of reference points392-402 located on the as-designed edge 360 of tooth 34 as seen in theprojection of the computer model. Operator 90 may similarly select sets364 and 366 of reference points on other 2D images (e.g. 96, 98)included in the subset of 2D images 78.

Returning to FIG. 8, method 2000 may include a step of projectingreference points on 3D point cloud 224. For example, processor 82 mayproject rays from positions (e.g. 46, 48, 50) through reference points372-384 and 392-402 in 2D images (e.g. 94, 96, 98). Processor 82 maydetermine locations corresponding to reference points 372-384 and392-402 on 3D point cloud 224 based on intersection of the rays usingoperations similar to those discussed above with respect to, forexample, step 1010 of method 1000.

Method 2000 may also include a step of determining amounts of wear (Step2016). For example, processor 82 may determine an image distance “d₁”between locations corresponding to, for example, reference points 372and 392 (see FIG. 10) on 3D point cloud 224. Similarly, for example,processor 82 may be configured to determine an image distance “d₂”between locations corresponding to, for example, reference points 378and 398 (see FIG. 10) on 3D point cloud 224. Processor 82 may also beconfigured to convert image distances d₁ and d₂ into amounts of wear Δ₁and Δ₂ based on a scaling factor S (e.g. Δ₁=S×d₁ and Δ₂=S×d₂). One ofordinary skill in the art would recognize that when 3D point cloud 244is scaled/rotated in step 2006, image distances d₁ and d₂ would beidentical to amounts of wear Δ₁ and Δ₂, and scaling factor S may be 1.One of ordinary skill in the art would also recognize that when the 3Dcomputer model is scaled/rotated in step 2006, image distances d₁ and d₂may be different from the amounts of wear Δ₁ and Δ₂ and scale factor Smay be different from 1.

Processor 82 may be configured to determine scaling factor S in avariety of ways. In one exemplary embodiment, processor may determinethe scaling factor S based on the known dimensions of a feature 340,which may exist on undercarriage 12, by performing processes similar tothose discussed above with respect to, for example, step 312 of method1000. In another exemplary embodiment, processor 82 may determine thescaling factor based on the known distance between two reference pointsin the computer model. For example, referring to FIG. 9, processor maydetermine scaling factor based on a distance between reference points392 and 402. Because reference points 392 and 402 lie on the computermodel outline 360 of tooth 34, processor 82 may determine a modeldistance “W” between reference points 392 and 402 based on the computermodel. Processor 82 may also determine a reference image distance “w”between locations corresponding to reference points 392 and 402 in 3Dpoint cloud 224. Processor 82 may determine scaling factor S based onthe model distance W and the reference image distance w (e.g. S=W/w).

Processor 82 may be also configured to compare the amounts of wear Δ₁and Δ₂ to a threshold amount of wear to determine whether sprocket wheel18 requires repair or replacement. It is also contemplated thatprocessor 82 may determine a single value representing the amount ofwear by averaging, taking the maximum of, or performing any othermathematical operations on the individual amounts of wear Δ₁, Δ₂, etc.Thus, by determining the amounts of wear without taking machine 10 outof service, method 2000 may allow operator 90 to schedule and performmaintenance on machine 10 in a more timely and cost effective manner.For example, operator 90 may use processor 82 to determine an amount oftime “t_(life),” after which amounts of wear Δ₁, Δ₂, etc. may exceed athreshold amount of wear Δ_(limit). One of ordinary skill in the artwould recognize that t_(life), may represent a remaining useful life ofa particular component, for example, sprocket wheel 18 on machine 10.Operator 90 may procure replacement parts and/or schedule maintenancefor machine 10 based on the determined amount of time t_(life). Thus,operator 90 may be able to help ensure that machine 10 is not scheduledfor maintenance too far in advance of the remaining useful life t_(life)or at a time that exceeds t_(life).

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the disclosed wearmeasurement system without departing from the scope of the disclosure.Other embodiments of the wear measurement system will be apparent tothose skilled in the art from consideration of the specification andpractice of the wear measurement system disclosed herein. It is intendedthat the specification and examples be considered as exemplary only,with a true scope of the disclosure being indicated by the followingclaims and their equivalents.

What is claimed is:
 1. A wear measurement system for a component,comprising: an imaging device configured to obtain a plurality oftwo-dimensional images of the component; and a controller configured to:generate a three-dimensional point cloud representing the componentbased on the two-dimensional images; overlay a computer model of thecomponent on the three-dimensional point cloud; project the computermodel on the two-dimensional images; select at least two referencepoints appearing in each of a subset of images selected from thetwo-dimensional images; determine locations of the at least tworeference points in the three-dimensional point cloud; determine animage distance between the locations; and determine an amount of wearbased on the image distance.
 2. The wear measurement system of claim 1,wherein the controller is further configured to: select at least onefeature appearing in each of the two-dimensional images; determinepositions and orientations of the imaging device for each of thetwo-dimensional images based on positions of the at least one feature inthe two-dimensional images; and generate the three-dimensional pointcloud based on the positions and orientations of the imaging device andthe positions of the at least one feature in the two-dimensional images.3. The wear measurement system of claim 2, wherein the controller isfurther configured to: select a first set of register points associatedwith features in the computer model; select a second set of registerpoints in the two-dimensional images, the second set of register pointscorresponding to the features associated with the first set of registerpoints; determine register point locations corresponding to the secondset in the three-dimensional point cloud; and overlay the computer modelon the three-dimensional point cloud by orienting the three-dimensionalpoint cloud such that the first set of register points in the computermodel overlap with the register point locations in the three-dimensionalpoint cloud.
 4. The wear measurement system of claim 3, wherein the atleast two reference points include: a first reference point positionedon a projection of the computer model on each of the subset of images,and a second reference point positioned on the component in each of thesubset of images.
 5. The wear measurement system of claim 4, wherein thecontroller is configured to identify the locations of the firstreference point and the second reference point by: determining positionsof the imaging device relative to the component for the subset ofimages; projecting rays from the determined positions of the imagingdevice through the at least two reference points; and determining thelocations of the first reference point and the second reference point inthe three-dimensional point cloud based on intersections of the rays. 6.The wear measurement system of claim 5, wherein the controller isfurther configured to: determine the image distance between thelocations of the first reference point and the second reference point;and convert the image distance into an actual distance based on ascaling factor.
 7. The wear measurement system of claim 6, wherein thescaling factor is based on a dimension determined from the computermodel.
 8. The wear measurement system of claim 7, wherein the controlleris configured to: select a first image from the subset; select a secondimage from the subset; project a first ray originating at a firstposition of the imaging device, corresponding to the first image, thefirst ray passing through the first reference point in the first image;project a second ray originating at a second position of the imagingdevice, corresponding to the second image, the second ray passingthrough the first reference point in the second image; and determine alocation of the first reference point in the three-dimensional pointcloud based on an intersection of the first ray and the second ray. 9.The wear measurement system of claim 8, wherein the controller isconfigured to determine the location in the three-dimensional pointcloud at which a distance between the first ray and the second ray isless than a threshold distance.
 10. The wear measurement system of claim2, wherein the controller is configured to automatically select the atleast one feature by comparing the two-dimensional images with an imageof the at least one feature stored in a memory associated with thecontroller.
 11. The wear measurement system of claim 2, furtherincluding an input device configured to receive inputs from an operator,wherein the controller is configured to select the at least one featurebased on the inputs received from the operator.
 12. A method ofmeasuring wear on a component, comprising: obtaining, using an imagingdevice, a plurality of two-dimensional images of the component;generating, using a controller, a three-dimensional point cloud of thecomponent based on the two-dimensional images; overlaying a computermodel of the component on the three-dimensional point cloud; projectingthe computer model on the two-dimensional images; selecting at least tworeference points appearing in each of a subset of images selected fromthe two-dimensional images; determining locations of the at least tworeference points in the three-dimensional point cloud; determining animage distance between the locations; and determining an amount of wearbased on the image distance.
 13. The method of claim 12, whereingenerating the three-dimensional point cloud includes: selecting atleast one feature that appears in each of the two-dimensional images;determining positions and orientations of the imaging device for each ofthe two-dimensional images based on positions of the at least onefeature in the two-dimensional images; and generating thethree-dimensional point cloud based on the positions and orientations ofthe imaging device and the positions of the at least one feature in thetwo-dimensional images.
 14. The method of claim 13, wherein selectingthe at least two reference points includes: selecting a first referencepoint positioned on a projection of the computer model on each of thesubset of images, and selecting a second reference point positioned onthe component in each of the subset of images.
 15. The method of claim14, wherein determining the locations of the two reference pointsincludes: selecting a first image from the subset; selecting a secondimage from the subset; projecting a first ray originating at a firstposition of the imaging device, corresponding to the first image, thefirst ray passing through the first reference point in the first image;projecting a second ray originating at a second position of the imagingdevice, corresponding to the second image, the second ray passingthrough the first reference point in the second image; projecting athird ray originating at the first position of the imaging device, thethird ray passing through the second reference point in the first image;projecting a fourth ray originating at the second position of theimaging device, the fourth ray passing through the second referencepoint in the second image; determining a first location of the firstreference point in the three-dimensional point cloud based on a firstintersection of the first ray and the second ray; and determining asecond location of the second reference point in the three-dimensionalpoint cloud based on a second intersection of the third ray and thefourth ray.
 16. The method of claim 15, wherein determining the firstlocation includes determining where a first distance between the firstray and the second ray is less than a threshold distance, anddetermining the second location includes determining where a seconddistance between the third ray and the fourth ray is less than thethreshold distance.
 17. The method of claim 16, wherein determining theamount of wear further includes: determining the image distance betweenthe first location and the second location; and converting the imagedistance into an actual distance using a scaling factor.
 18. The methodof claim 17, further including: selecting a third reference pointpositioned on the projection of the computer model on each of the subsetof images; selecting a fourth reference point positioned on theprojection of the computer model on each of the subset of images;determining a third location of the third reference point in thethree-dimensional point cloud; determining a fourth location of thefourth reference point in the three-dimensional point cloud; determininga reference image distance between the third location and the fourthlocation; determining a model distance between the third reference pointand the fourth reference point from the computer model; and determiningthe scaling factor as a ratio of the model distance and the referenceimage distance.
 19. The method of claim 12, wherein selecting the atleast one feature includes comparing the two-dimensional images with animage of the at least one feature stored in a memory associated with thecontroller.
 20. The method of claim 12, wherein selecting the at leastone feature includes selecting, using an input device, the at least onefeature in the plurality of two-dimensional images.